@inproceedings{zhai-etal-2026-nullify,
title = "Nullify: Null-Space Activation Steering for Training-Free {LLM} Unlearning",
author = "Zhai, Wei and
Liu, Xiang and
Huang, Qiang and
Qian, Rui and
Liu, Lemao and
Li, Ziwei and
Wang, Ziqi and
Huang, Zhitao and
Dou, Dejing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.874/",
pages = "20213--20225",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase specific knowledge to prevent privacy leakage with minimal loss of model utility. However, existing methods struggle to balance forget quality with utility, and typically incur substantial computational costs due to parameter fine-tuning. To address this, we propose Nullify, a training-free, non-destructive activation steering method for LLM unlearning. Nullify employs steering vectors during inference to redirect privacy-related activations away from their memorized answers, while satisfying a null-space constraint that leaves retained-query activations essentially unaffected to maintain utility. Evaluations on benchmarks including TOFU and MUSE show that Nullify matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility. By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework."
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<abstract>Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase specific knowledge to prevent privacy leakage with minimal loss of model utility. However, existing methods struggle to balance forget quality with utility, and typically incur substantial computational costs due to parameter fine-tuning. To address this, we propose Nullify, a training-free, non-destructive activation steering method for LLM unlearning. Nullify employs steering vectors during inference to redirect privacy-related activations away from their memorized answers, while satisfying a null-space constraint that leaves retained-query activations essentially unaffected to maintain utility. Evaluations on benchmarks including TOFU and MUSE show that Nullify matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility. By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework.</abstract>
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%0 Conference Proceedings
%T Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning
%A Zhai, Wei
%A Liu, Xiang
%A Huang, Qiang
%A Qian, Rui
%A Liu, Lemao
%A Li, Ziwei
%A Wang, Ziqi
%A Huang, Zhitao
%A Dou, Dejing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhai-etal-2026-nullify
%X Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase specific knowledge to prevent privacy leakage with minimal loss of model utility. However, existing methods struggle to balance forget quality with utility, and typically incur substantial computational costs due to parameter fine-tuning. To address this, we propose Nullify, a training-free, non-destructive activation steering method for LLM unlearning. Nullify employs steering vectors during inference to redirect privacy-related activations away from their memorized answers, while satisfying a null-space constraint that leaves retained-query activations essentially unaffected to maintain utility. Evaluations on benchmarks including TOFU and MUSE show that Nullify matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility. By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework.
%U https://aclanthology.org/2026.emnlp-main.874/
%P 20213-20225
Markdown (Informal)
[Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning](https://aclanthology.org/2026.emnlp-main.874/) (Zhai et al., EMNLP 2026)
ACL
- Wei Zhai, Xiang Liu, Qiang Huang, Rui Qian, Lemao Liu, Ziwei Li, Ziqi Wang, Zhitao Huang, and Dejing Dou. 2026. Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20213–20225, Budapest, Hungary. Association for Computational Linguistics.